Fault diagnosis of analog circuit for WPA-IGA-BP neural network

被引：7

作者：

Wang L. ^{[1
]}

Liu Z. ^{[2
]}

机构：

[1] Vocational Technical Institute, Civil Aviation University of China, Tianjin

[2] College of Electronic Information and Automation, Civil Aviation University of China, Tianjin

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2021年 / 43卷 / 04期

关键词：

Analog circuit; Back propagation (BP) neural network; Fault diagnosis; Feature extraction; Immune genetic algorithm (IGA);

D O I：

10.12305/j.issn.1001-506X.2021.04.32

中图分类号：

学科分类号：

摘要：

In the view of the difficulty in feature extraction and failure signal classification in analog circuit with gradual change, an immune genetic algorithm (IGA) is proposed to optimize the parameter optimization process in back propagation (BP) neural network, so as to realize analog circuit fault diagnosis. Firstly, the wavelet package analysis (WPA) is used to decompose and reconstruct the output response of analog circuit in four layers, and the feature vector is extracted. Then, the IGA optimized BP neural network is used for training and testing to realize fault diagnosis of different faults. Finally, the two simulation methods are verified by simulation. The experimental results show that, compared with the BP neural network before optimization, the proposed method improves the accuracy of fault diagnosis by about 15%. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.

引用

页码：1133 / 1143

页数：10

共 31 条

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